Antenna Season Report Notebook¶

Josh Dillon, Last Revised January 2022

This notebook examines an individual antenna's performance over a whole season. This notebook parses information from each nightly rtp_summarynotebook (as saved to .csvs) and builds a table describing antenna performance. It also reproduces per-antenna plots from each auto_metrics notebook pertinent to the specific antenna.

In [1]:
import os
from IPython.display import display, HTML
display(HTML("<style>.container { width:100% !important; }</style>"))
In [2]:
# If you want to run this notebook locally, copy the output of the next cell into the next line of this cell.
# antenna = "004"
# csv_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/_rtp_summary_'
# auto_metrics_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/auto_metrics_inspect'
# os.environ["ANTENNA"] = antenna
# os.environ["CSV_FOLDER"] = csv_folder
# os.environ["AUTO_METRICS_FOLDER"] = auto_metrics_folder
In [3]:
# Use environment variables to figure out path to the csvs and auto_metrics
antenna = str(int(os.environ["ANTENNA"]))
csv_folder = os.environ["CSV_FOLDER"]
auto_metrics_folder = os.environ["AUTO_METRICS_FOLDER"]
print(f'antenna = "{antenna}"')
print(f'csv_folder = "{csv_folder}"')
print(f'auto_metrics_folder = "{auto_metrics_folder}"')

antenna = "85"
csv_folder = "/home/obs/src/H6C_Notebooks/_rtp_summary_"
auto_metrics_folder = "/home/obs/src/H6C_Notebooks/auto_metrics_inspect"
In [4]:
display(HTML(f'<h1 style=font-size:50px><u>Antenna {antenna} Report</u><p></p></h1>'))

Antenna 85 Report

In [5]:
import numpy as np
import pandas as pd
pd.set_option('display.max_rows', 1000)
import glob
import re
from hera_notebook_templates.utils import status_colors, Antenna
In [6]:
# load csvs and auto_metrics htmls in reverse chronological order
csvs = sorted(glob.glob(os.path.join(csv_folder, 'rtp_summary_table*.csv')))[::-1]
print(f'Found {len(csvs)} csvs in {csv_folder}')
auto_metric_htmls = sorted(glob.glob(auto_metrics_folder + '/auto_metrics_inspect_*.html'))[::-1]
print(f'Found {len(auto_metric_htmls)} auto_metrics notebooks in {auto_metrics_folder}')

Found 30 csvs in /home/obs/src/H6C_Notebooks/_rtp_summary_
Found 28 auto_metrics notebooks in /home/obs/src/H6C_Notebooks/auto_metrics_inspect
In [7]:
# Per-season options
mean_round_modz_cut = 4
dead_cut = 0.4
crossed_cut = 0.0

def jd_to_summary_url(jd):
    return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H6C_Notebooks/blob/main/_rtp_summary_/rtp_summary_{jd}.html'

def jd_to_auto_metrics_url(jd):
    return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H6C_Notebooks/blob/main/auto_metrics_inspect/auto_metrics_inspect_{jd}.html'

Load relevant info from summary CSVs¶

In [8]:
this_antenna = None
jds = []

# parse information about antennas and nodes
for csv in csvs:
    df = pd.read_csv(csv)
    for n in range(len(df)):
        # Add this day to the antenna
        row = df.loc[n]
        if isinstance(row['Ant'], str) and '<a href' in row['Ant']:
            antnum = int(row['Ant'].split('</a>')[0].split('>')[-1]) # it's a link, extract antnum
        else:
            antnum = int(row['Ant'])
        if antnum != int(antenna):
            continue
        
        if np.issubdtype(type(row['Node']), np.integer):
            row['Node'] = str(row['Node'])
        if type(row['Node']) == str and row['Node'].isnumeric():
            row['Node'] = 'N' + ('0' if len(row['Node']) == 1 else '') + row['Node']
            
        if this_antenna is None:
            this_antenna = Antenna(row['Ant'], row['Node'])
        jd = [int(s) for s in re.split('_|\.', csv) if s.isdigit()][-1]
        jds.append(jd)
        this_antenna.add_day(jd, row)
        break
In [9]:
# build dataframe
to_show = {'JDs': [f'<a href="{jd_to_summary_url(jd)}" target="_blank">{jd}</a>' for jd in jds]}
to_show['A Priori Status'] = [this_antenna.statuses[jd] for jd in jds]

df = pd.DataFrame(to_show)

# create bar chart columns for flagging percentages:
bar_cols = {}
bar_cols['Auto Metrics Flags'] = [this_antenna.auto_flags[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jee)'] = [this_antenna.dead_flags_Jee[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jnn)'] = [this_antenna.dead_flags_Jnn[jd] for jd in jds]
bar_cols['Crossed Fraction in Ant Metrics'] = [this_antenna.crossed_flags[jd] for jd in jds]
bar_cols['Flag Fraction Before Redcal'] = [this_antenna.flags_before_redcal[jd] for jd in jds]
bar_cols['Flagged By Redcal chi^2 Fraction'] = [this_antenna.redcal_flags[jd] for jd in jds]
for col in bar_cols:
    df[col] = bar_cols[col]

z_score_cols = {}
z_score_cols['ee Shape Modified Z-Score'] = [this_antenna.ee_shape_zs[jd] for jd in jds]
z_score_cols['nn Shape Modified Z-Score'] = [this_antenna.nn_shape_zs[jd] for jd in jds]
z_score_cols['ee Power Modified Z-Score'] = [this_antenna.ee_power_zs[jd] for jd in jds]
z_score_cols['nn Power Modified Z-Score'] = [this_antenna.nn_power_zs[jd] for jd in jds]
z_score_cols['ee Temporal Variability Modified Z-Score'] = [this_antenna.ee_temp_var_zs[jd] for jd in jds]
z_score_cols['nn Temporal Variability Modified Z-Score'] = [this_antenna.nn_temp_var_zs[jd] for jd in jds]
z_score_cols['ee Temporal Discontinuties Modified Z-Score'] = [this_antenna.ee_temp_discon_zs[jd] for jd in jds]
z_score_cols['nn Temporal Discontinuties Modified Z-Score'] = [this_antenna.nn_temp_discon_zs[jd] for jd in jds]
for col in z_score_cols:
    df[col] = z_score_cols[col]

ant_metrics_cols = {}
ant_metrics_cols['Average Dead Ant Metric (Jee)'] = [this_antenna.Jee_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Dead Ant Metric (Jnn)'] = [this_antenna.Jnn_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Crossed Ant Metric'] = [this_antenna.crossed_metrics[jd] for jd in jds]
for col in ant_metrics_cols:
    df[col] = ant_metrics_cols[col]

redcal_cols = {}
redcal_cols['Median chi^2 Per Antenna (Jee)'] = [this_antenna.Jee_chisqs[jd] for jd in jds]
redcal_cols['Median chi^2 Per Antenna (Jnn)'] = [this_antenna.Jnn_chisqs[jd] for jd in jds]   
for col in redcal_cols:
    df[col] = redcal_cols[col]

# style dataframe
table = df.style.hide_index()\
          .applymap(lambda val: f'background-color: {status_colors[val]}' if val in status_colors else '', subset=['A Priori Status']) \
          .background_gradient(cmap='viridis', vmax=mean_round_modz_cut * 3, vmin=0, axis=None, subset=list(z_score_cols.keys())) \
          .background_gradient(cmap='bwr_r', vmin=dead_cut-.25, vmax=dead_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
          .background_gradient(cmap='bwr_r', vmin=crossed_cut-.25, vmax=crossed_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
          .background_gradient(cmap='plasma', vmax=4, vmin=1, axis=None, subset=list(redcal_cols.keys())) \
          .applymap(lambda val: 'font-weight: bold' if val < dead_cut else '', subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
          .applymap(lambda val: 'font-weight: bold' if val < crossed_cut else '', subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
          .applymap(lambda val: 'font-weight: bold' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
          .applymap(lambda val: 'color: red' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
          .bar(subset=list(bar_cols.keys()), vmin=0, vmax=1) \
          .format({col: '{:,.4f}'.format for col in z_score_cols}) \
          .format({col: '{:,.4f}'.format for col in ant_metrics_cols}) \
          .format('{:,.2%}', na_rep='-', subset=list(bar_cols.keys())) \
          .set_table_styles([dict(selector="th",props=[('max-width', f'70pt')])])

Table 1: Per-Night RTP Summary Info For This Atenna¶

This table reproduces each night's row for this antenna from the RTP Summary notebooks. For more info on the columns, see those notebooks, linked in the JD column.

In [10]:
display(HTML(f'<h2>Antenna {antenna}, Node {this_antenna.node}:</h2>'))
HTML(table.render(render_links=True, escape=False))

Antenna 85, Node N08:

Out[10]:
JDs A Priori Status Auto Metrics Flags Dead Fraction in Ant Metrics (Jee) Dead Fraction in Ant Metrics (Jnn) Crossed Fraction in Ant Metrics Flag Fraction Before Redcal Flagged By Redcal chi^2 Fraction ee Shape Modified Z-Score nn Shape Modified Z-Score ee Power Modified Z-Score nn Power Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Discontinuties Modified Z-Score nn Temporal Discontinuties Modified Z-Score Average Dead Ant Metric (Jee) Average Dead Ant Metric (Jnn) Average Crossed Ant Metric Median chi^2 Per Antenna (Jee) Median chi^2 Per Antenna (Jnn)
2459846 digital_ok 0.00% 0.00% 0.00% 0.00% 33.33% 0.00% 0.028253 -0.025898 0.443296 0.819595 -0.185583 -0.876291 -0.665843 -0.866190 0.8499 0.7055 0.4593 1.435143 1.344681
2459845 digital_ok 100.00% 100.00% 100.00% 0.00% 100.00% 0.00% 388.825925 388.347279 inf inf 7552.255304 8082.448152 6650.939724 7871.138679 nan nan nan 0.000000 0.000000
2459844 digital_ok 100.00% 100.00% 100.00% 0.00% - - nan nan inf inf nan nan nan nan nan nan nan nan nan
2459842 digital_ok 100.00% 0.00% 0.00% 0.00% 100.00% 0.00% -1.357133 1.136537 -0.186901 4.748959 -0.194935 -2.008977 -0.167095 0.333058 0.7588 0.6188 0.3084 7.140056 4.412244
2459841 digital_ok 100.00% 100.00% 100.00% 0.00% - - 1.509472 12.502050 9.916534 7.761031 0.433890 -0.419165 0.044543 0.735802 0.0397 0.0344 0.0045 nan nan
2459840 digital_ok 100.00% 100.00% 100.00% 0.00% - - nan nan inf inf nan nan nan nan nan nan nan nan nan
2459839 digital_ok 100.00% - - - - - -0.547983 -0.529218 18.838595 20.535303 1.782679 1.084721 -1.775160 -1.188788 nan nan nan nan nan
2459838 digital_ok 100.00% 0.00% 0.00% 0.00% 100.00% 0.00% -0.535101 4.178837 -0.274144 10.471804 0.056716 11.407992 -0.568671 1.012804 0.7509 0.6465 0.4255 4.331196 3.201481
2459836 digital_ok - 100.00% 100.00% 0.00% - - nan nan nan nan nan nan nan nan 0.0558 0.0382 0.0024 nan nan
2459835 digital_ok 0.00% 100.00% 100.00% 0.00% - - -1.319108 -1.360946 -0.709159 -0.486577 -0.623559 -1.352756 -0.290917 0.632656 0.0558 0.0380 0.0023 nan nan
2459833 digital_ok 0.00% 100.00% 100.00% 0.00% - - 0.993755 1.490117 2.281758 2.453097 0.842391 -0.321412 -1.368273 -0.657719 0.0291 0.0274 0.0013 nan nan
2459832 digital_ok 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.147073 -0.054670 -0.919114 -0.805396 0.080354 -1.439282 -0.445875 -1.108229 0.8074 0.5781 0.5565 1.569949 1.585531
2459831 digital_ok 100.00% 100.00% 100.00% 0.00% - - -0.085276 -0.366573 21.468412 22.911670 4.185265 3.309194 -1.621337 -1.460901 0.0279 0.0263 0.0017 nan nan
2459830 digital_ok 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.380433 0.421474 0.490739 1.610929 -1.023172 -1.795278 -0.319426 -1.021962 0.8075 0.5907 0.5336 1.449632 1.127851
2459829 digital_ok 0.00% 0.00% 0.00% 0.00% 0.65% 75.82% 0.018479 0.122469 1.043228 1.431996 -0.203692 -0.540061 -0.097247 -0.656496 0.7541 0.6847 0.3905 inf inf
2459828 digital_ok 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.652657 0.124433 1.065577 1.758454 -0.813295 -1.178219 -0.234884 0.048297 0.8066 0.5939 0.5185 1.552021 1.565384
2459827 digital_ok 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.025917 0.153769 -0.594153 -0.742403 -1.020248 -0.970354 -1.015512 -1.311352 0.7598 0.6947 0.3941 1.562885 1.408643
2459826 digital_ok 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.578945 0.920453 0.763259 1.852152 -1.505742 -1.162741 -0.609819 -0.749105 0.8040 0.6128 0.5022 1.324825 1.139889
2459825 digital_ok 0.00% 0.00% 0.00% 0.00% 0.00% 2.63% 0.480456 1.117607 0.920506 1.669366 -0.544759 -1.099840 -0.904694 -0.931780 0.8061 0.6310 0.4892 1.910484 2.017325
2459824 digital_ok 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% -0.539610 -0.044645 -0.814135 -0.916405 -0.439192 -0.757162 -0.640125 -0.949430 0.7300 0.7544 0.3491 1.582838 1.710827
2459823 digital_ok 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.283099 0.530801 0.912609 1.779007 -0.494278 -0.405466 0.070111 -0.001491 0.7713 0.6767 0.4353 1.812637 1.808984
2459822 digital_ok 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.577993 0.548634 -0.872719 -0.332280 -1.346205 -1.536986 -0.466625 -1.110454 0.8169 0.6584 0.4968 2.010686 1.512946
2459821 digital_ok 0.00% 0.00% 0.00% 0.00% 0.00% 31.58% 1.014536 1.025853 1.055705 1.851694 -1.152781 -1.002509 -1.613311 -1.564949 0.8236 0.6862 0.4723 1.727762 1.562669
2459820 digital_ok 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.146668 0.973415 -0.858772 -0.656308 -0.545388 -1.216118 -0.747063 -0.970555 0.7859 0.7267 0.3893 1.619851 1.420103
2459817 digital_ok 0.00% 0.00% 0.00% 0.00% 0.00% 63.16% 0.401127 -0.053730 -0.855365 -0.478684 -1.630819 -1.727128 -0.638469 -0.732442 0.8346 0.7219 0.4751 2.306226 2.215491
2459816 digital_ok 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.683823 0.289683 -1.041939 -0.661196 -1.406629 -0.795673 -0.701225 -0.960724 0.8538 0.6454 0.5555 1.794319 1.515242
2459815 digital_ok 0.00% 0.00% 0.00% 0.00% 0.00% 42.11% 0.538403 0.361197 -0.975091 -0.688764 -1.223639 -1.628758 -0.860117 -0.666073 0.8363 0.7370 0.4776 1.868253 1.691892
2459814 digital_ok 0.00% - - - - - nan nan nan nan nan nan nan nan nan nan nan nan nan
2459813 digital_ok 0.00% 100.00% 100.00% 0.00% 100.00% 0.00% 0.138552 0.516021 0.531885 0.767410 0.446822 -1.329330 -0.124978 -0.091760 0.0850 0.0882 0.0142 1.162172 1.164490

Load antenna metric spectra and waterfalls from auto_metrics notebooks.¶

In [11]:
htmls_to_display = []
for am_html in auto_metric_htmls:
    html_to_display = ''
    # read html into a list of lines
    with open(am_html) as f:
        lines = f.readlines()
    
    # find section with this antenna's metric plots and add to html_to_display
    jd = [int(s) for s in re.split('_|\.', am_html) if s.isdigit()][-1]
    try:
        section_start_line = lines.index(f'<h2>Antenna {antenna}: {jd}</h2>\n')
    except ValueError:
        continue
    html_to_display += lines[section_start_line].replace(str(jd), f'<a href="{jd_to_auto_metrics_url(jd)}" target="_blank">{jd}</a>')
    for line in lines[section_start_line + 1:]:
        html_to_display += line
        if '<hr' in line:
            htmls_to_display.append(html_to_display)
            break

Figure 1: Antenna autocorrelation metric spectra and waterfalls.¶

These figures are reproduced from auto_metrics notebooks. For more info on the specific plots and metrics, see those notebooks (linked at the JD). The most recent 100 days (at most) are shown.

In [12]:
for i, html_to_display in enumerate(htmls_to_display):
    if i == 100:
        break
    display(HTML(html_to_display))

Antenna 85: 2459846

Ant Node A Priori Status Worst Metric Worst Modified Z-Score ee Shape Modified Z-Score nn Shape Modified Z-Score ee Power Modified Z-Score nn Power Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Discontinuties Modified Z-Score nn Temporal Discontinuties Modified Z-Score
85 N08 digital_ok nn Power 0.819595 0.028253 -0.025898 0.443296 0.819595 -0.185583 -0.876291 -0.665843 -0.866190

Antenna 85: 2459845

Ant Node A Priori Status Worst Metric Worst Modified Z-Score nn Shape Modified Z-Score ee Shape Modified Z-Score nn Power Modified Z-Score ee Power Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Discontinuties Modified Z-Score ee Temporal Discontinuties Modified Z-Score
85 N08 digital_ok nn Power inf 388.347279 388.825925 inf inf 8082.448152 7552.255304 7871.138679 6650.939724

Antenna 85: 2459844

Ant Node A Priori Status Worst Metric Worst Modified Z-Score ee Shape Modified Z-Score nn Shape Modified Z-Score ee Power Modified Z-Score nn Power Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Discontinuties Modified Z-Score nn Temporal Discontinuties Modified Z-Score
85 N08 digital_ok ee Shape nan nan nan inf inf nan nan nan nan

Antenna 85: 2459842

Ant Node A Priori Status Worst Metric Worst Modified Z-Score ee Shape Modified Z-Score nn Shape Modified Z-Score ee Power Modified Z-Score nn Power Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Discontinuties Modified Z-Score nn Temporal Discontinuties Modified Z-Score
85 N08 digital_ok nn Power 4.748959 -1.357133 1.136537 -0.186901 4.748959 -0.194935 -2.008977 -0.167095 0.333058

Antenna 85: 2459841

Ant Node A Priori Status Worst Metric Worst Modified Z-Score ee Shape Modified Z-Score nn Shape Modified Z-Score ee Power Modified Z-Score nn Power Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Discontinuties Modified Z-Score nn Temporal Discontinuties Modified Z-Score
85 N08 digital_ok nn Shape 12.502050 1.509472 12.502050 9.916534 7.761031 0.433890 -0.419165 0.044543 0.735802

Antenna 85: 2459840

Ant Node A Priori Status Worst Metric Worst Modified Z-Score ee Shape Modified Z-Score nn Shape Modified Z-Score ee Power Modified Z-Score nn Power Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Discontinuties Modified Z-Score nn Temporal Discontinuties Modified Z-Score
85 N08 digital_ok ee Shape nan nan nan inf inf nan nan nan nan

Antenna 85: 2459839

Ant Node A Priori Status Worst Metric Worst Modified Z-Score nn Shape Modified Z-Score ee Shape Modified Z-Score nn Power Modified Z-Score ee Power Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Discontinuties Modified Z-Score ee Temporal Discontinuties Modified Z-Score
85 N08 digital_ok nn Power 20.535303 -0.529218 -0.547983 20.535303 18.838595 1.084721 1.782679 -1.188788 -1.775160

Antenna 85: 2459838

Ant Node A Priori Status Worst Metric Worst Modified Z-Score nn Shape Modified Z-Score ee Shape Modified Z-Score nn Power Modified Z-Score ee Power Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Discontinuties Modified Z-Score ee Temporal Discontinuties Modified Z-Score
85 N08 digital_ok nn Temporal Variability 11.407992 4.178837 -0.535101 10.471804 -0.274144 11.407992 0.056716 1.012804 -0.568671

Antenna 85: 2459835

Ant Node A Priori Status Worst Metric Worst Modified Z-Score nn Shape Modified Z-Score ee Shape Modified Z-Score nn Power Modified Z-Score ee Power Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Discontinuties Modified Z-Score ee Temporal Discontinuties Modified Z-Score
85 N08 digital_ok nn Temporal Discontinuties 0.632656 -1.360946 -1.319108 -0.486577 -0.709159 -1.352756 -0.623559 0.632656 -0.290917

Antenna 85: 2459833

Ant Node A Priori Status Worst Metric Worst Modified Z-Score nn Shape Modified Z-Score ee Shape Modified Z-Score nn Power Modified Z-Score ee Power Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Discontinuties Modified Z-Score ee Temporal Discontinuties Modified Z-Score
85 N08 digital_ok nn Power 2.453097 1.490117 0.993755 2.453097 2.281758 -0.321412 0.842391 -0.657719 -1.368273

Antenna 85: 2459832

Ant Node A Priori Status Worst Metric Worst Modified Z-Score ee Shape Modified Z-Score nn Shape Modified Z-Score ee Power Modified Z-Score nn Power Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Discontinuties Modified Z-Score nn Temporal Discontinuties Modified Z-Score
85 N08 digital_ok ee Shape 0.147073 0.147073 -0.054670 -0.919114 -0.805396 0.080354 -1.439282 -0.445875 -1.108229

Antenna 85: 2459831

Ant Node A Priori Status Worst Metric Worst Modified Z-Score ee Shape Modified Z-Score nn Shape Modified Z-Score ee Power Modified Z-Score nn Power Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Discontinuties Modified Z-Score nn Temporal Discontinuties Modified Z-Score
85 N08 digital_ok nn Power 22.911670 -0.085276 -0.366573 21.468412 22.911670 4.185265 3.309194 -1.621337 -1.460901

Antenna 85: 2459830

Ant Node A Priori Status Worst Metric Worst Modified Z-Score ee Shape Modified Z-Score nn Shape Modified Z-Score ee Power Modified Z-Score nn Power Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Discontinuties Modified Z-Score nn Temporal Discontinuties Modified Z-Score
85 N08 digital_ok nn Power 1.610929 0.380433 0.421474 0.490739 1.610929 -1.023172 -1.795278 -0.319426 -1.021962

Antenna 85: 2459829

Ant Node A Priori Status Worst Metric Worst Modified Z-Score nn Shape Modified Z-Score ee Shape Modified Z-Score nn Power Modified Z-Score ee Power Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Discontinuties Modified Z-Score ee Temporal Discontinuties Modified Z-Score
85 N08 digital_ok nn Power 1.431996 0.122469 0.018479 1.431996 1.043228 -0.540061 -0.203692 -0.656496 -0.097247

Antenna 85: 2459828

Ant Node A Priori Status Worst Metric Worst Modified Z-Score nn Shape Modified Z-Score ee Shape Modified Z-Score nn Power Modified Z-Score ee Power Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Discontinuties Modified Z-Score ee Temporal Discontinuties Modified Z-Score
85 N08 digital_ok nn Power 1.758454 0.124433 0.652657 1.758454 1.065577 -1.178219 -0.813295 0.048297 -0.234884

Antenna 85: 2459827

Ant Node A Priori Status Worst Metric Worst Modified Z-Score ee Shape Modified Z-Score nn Shape Modified Z-Score ee Power Modified Z-Score nn Power Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Discontinuties Modified Z-Score nn Temporal Discontinuties Modified Z-Score
85 N08 digital_ok nn Shape 0.153769 0.025917 0.153769 -0.594153 -0.742403 -1.020248 -0.970354 -1.015512 -1.311352

Antenna 85: 2459826

Ant Node A Priori Status Worst Metric Worst Modified Z-Score nn Shape Modified Z-Score ee Shape Modified Z-Score nn Power Modified Z-Score ee Power Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Discontinuties Modified Z-Score ee Temporal Discontinuties Modified Z-Score
85 N08 digital_ok nn Power 1.852152 0.920453 0.578945 1.852152 0.763259 -1.162741 -1.505742 -0.749105 -0.609819

Antenna 85: 2459825

Ant Node A Priori Status Worst Metric Worst Modified Z-Score nn Shape Modified Z-Score ee Shape Modified Z-Score nn Power Modified Z-Score ee Power Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Discontinuties Modified Z-Score ee Temporal Discontinuties Modified Z-Score
85 N08 digital_ok nn Power 1.669366 1.117607 0.480456 1.669366 0.920506 -1.099840 -0.544759 -0.931780 -0.904694

Antenna 85: 2459824

Ant Node A Priori Status Worst Metric Worst Modified Z-Score ee Shape Modified Z-Score nn Shape Modified Z-Score ee Power Modified Z-Score nn Power Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Discontinuties Modified Z-Score nn Temporal Discontinuties Modified Z-Score
85 N08 digital_ok nn Shape -0.044645 -0.539610 -0.044645 -0.814135 -0.916405 -0.439192 -0.757162 -0.640125 -0.949430

Antenna 85: 2459823

Ant Node A Priori Status Worst Metric Worst Modified Z-Score nn Shape Modified Z-Score ee Shape Modified Z-Score nn Power Modified Z-Score ee Power Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Discontinuties Modified Z-Score ee Temporal Discontinuties Modified Z-Score
85 N08 digital_ok nn Power 1.779007 0.530801 0.283099 1.779007 0.912609 -0.405466 -0.494278 -0.001491 0.070111

Antenna 85: 2459822

Ant Node A Priori Status Worst Metric Worst Modified Z-Score ee Shape Modified Z-Score nn Shape Modified Z-Score ee Power Modified Z-Score nn Power Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Discontinuties Modified Z-Score nn Temporal Discontinuties Modified Z-Score
85 N08 digital_ok ee Shape 0.577993 0.577993 0.548634 -0.872719 -0.332280 -1.346205 -1.536986 -0.466625 -1.110454

Antenna 85: 2459821

Ant Node A Priori Status Worst Metric Worst Modified Z-Score nn Shape Modified Z-Score ee Shape Modified Z-Score nn Power Modified Z-Score ee Power Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Discontinuties Modified Z-Score ee Temporal Discontinuties Modified Z-Score
85 N08 digital_ok nn Power 1.851694 1.025853 1.014536 1.851694 1.055705 -1.002509 -1.152781 -1.564949 -1.613311

Antenna 85: 2459820

Ant Node A Priori Status Worst Metric Worst Modified Z-Score ee Shape Modified Z-Score nn Shape Modified Z-Score ee Power Modified Z-Score nn Power Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Discontinuties Modified Z-Score nn Temporal Discontinuties Modified Z-Score
85 N08 digital_ok nn Shape 0.973415 0.146668 0.973415 -0.858772 -0.656308 -0.545388 -1.216118 -0.747063 -0.970555

Antenna 85: 2459817

Ant Node A Priori Status Worst Metric Worst Modified Z-Score ee Shape Modified Z-Score nn Shape Modified Z-Score ee Power Modified Z-Score nn Power Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Discontinuties Modified Z-Score nn Temporal Discontinuties Modified Z-Score
85 N08 digital_ok ee Shape 0.401127 0.401127 -0.053730 -0.855365 -0.478684 -1.630819 -1.727128 -0.638469 -0.732442

Antenna 85: 2459816

Ant Node A Priori Status Worst Metric Worst Modified Z-Score nn Shape Modified Z-Score ee Shape Modified Z-Score nn Power Modified Z-Score ee Power Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Discontinuties Modified Z-Score ee Temporal Discontinuties Modified Z-Score
85 N08 digital_ok ee Shape 0.683823 0.289683 0.683823 -0.661196 -1.041939 -0.795673 -1.406629 -0.960724 -0.701225

Antenna 85: 2459815

Ant Node A Priori Status Worst Metric Worst Modified Z-Score nn Shape Modified Z-Score ee Shape Modified Z-Score nn Power Modified Z-Score ee Power Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Discontinuties Modified Z-Score ee Temporal Discontinuties Modified Z-Score
85 N08 digital_ok ee Shape 0.538403 0.361197 0.538403 -0.688764 -0.975091 -1.628758 -1.223639 -0.666073 -0.860117

Antenna 85: 2459814

Ant Node A Priori Status Worst Metric Worst Modified Z-Score nn Shape Modified Z-Score ee Shape Modified Z-Score nn Power Modified Z-Score ee Power Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Discontinuties Modified Z-Score ee Temporal Discontinuties Modified Z-Score
85 N08 digital_ok nn Shape nan nan nan nan nan nan nan nan nan

Antenna 85: 2459813

Ant Node A Priori Status Worst Metric Worst Modified Z-Score nn Shape Modified Z-Score ee Shape Modified Z-Score nn Power Modified Z-Score ee Power Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Discontinuties Modified Z-Score ee Temporal Discontinuties Modified Z-Score
85 N08 digital_ok nn Power 0.767410 0.516021 0.138552 0.767410 0.531885 -1.329330 0.446822 -0.091760 -0.124978
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